Over the past several years, some network operations have embraced the notion of virtualization to address elasticity and/or other dynamic aspects of network demand. For example, creation and scaling of some services have evolved over the past several years from a first model that can be based on deploying dedicated and closely tied hardware and software to networks, to a second model that can instead be based upon embracing virtualization of services and service components.
In one approach to virtualization, network appliances can be emulated by virtual copies of those appliances. Thus, instead of a physical server with a particular application being deployed to a network, one approach to virtualization can entail creating a copy of the physical server and application by way of a virtual machine that executes the application and deploying this virtual copy to the network. The services therefore can be installed on and hosted by commercial-off-the-shelf (“COTS”) hardware, which can reduce costs and lead time for deploying an application, as well as allowing flexibility in terms of scaling, locating, and/or using the services.
One drawback, however, is that processing and memory storage resources associated with the hardware are still at a premium and still are required to provide the functionality that is being virtualized. In fact, because it has become easier to deploy new services and applications, the demand for these flexible resources has become higher, which in turn has put pressure on processing and memory resources needed to support various new applications and/or services.